1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00140838" target="_blank" >RIV/00216224:14330/25:00140838 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-031-82703-7_5" target="_blank" >http://dx.doi.org/10.1007/978-3-031-82703-7_5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-82703-7_5" target="_blank" >10.1007/978-3-031-82703-7_5</a>

Result language

angličtina

Original language name

1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization

Original language description

Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when instantiating parameterized Markov decision processes (MDPs) even with moderate values. Synthesizing policies for such huge MDPs is beyond the reach of available tools. We propose a learning-based approach to obtain a reasonable policy for such huge MDPs. The idea is to generalize optimal policies obtained by model-checking small instances to larger ones using decision-tree learning. Consequently, our method bypasses the need for explicit state-space exploration of large models, providing a practical solution to the state-space explosion problem. We demonstrate the efficacy of our approach by performing extensive experimentation on the relevant models from the quantitative verification benchmark set. The experimental results indicate that our policies perform well, even when the size of the model is orders of magnitude beyond the reach of state-of-the-art analysis tools.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

VMCAI 2025, 26th International Conference on Verification, Model Checking, and Abstract Interpretation

ISBN

9783031827020

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

24

Pages from-to

97-120

Publisher name

Springer

Place of publication

Denver, USA

Event location

Denver, USA

Event date

Jan 1, 2025

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

001446577100005